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These are the details for my current pipeline System Description: Hybrid Search pipeline combining dense embeddings (VoyageAI voyage-4-large 2048-dim) via Alibaba's Zvec database, with sparse lexical retrieval (BM25), fused together and passed through a cross-encoder reranker (VoyageAI Rerank-2.5). System Type: Hybrid / RAG Retriever Open Source: Not open source (rerankers and the embedding models are closed source, vector db and sparse retrieval is open source)
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@nimit2801 @prakhar7651 The description validation workflow is failing I am unable to find any suitable template in the readme. Do i have to follow any official template for the submission? |
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hey @shrey2003 Kindly add this in your PR description: https://app.devrev.ai/devrev/works/ISS-269621 |
shrey2003
changed the title
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Hey Shreya! |
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Hey @prakhar7651 thanks for your evaluation! |
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As per the instructions this json contains my results: test_queries_results.json @prakhar7651 @nimit2801 |
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Hey! |
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@prakhar7651 Thanks for the evaluation. I have tried improving my script and rerun the results there were few bugs affecting he results which i found out. Can I resubmit after improving? |
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Yes, you can. Let me know when you're done and tell me which file to evaluate. |
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test_queries_results_new.json @prakhar7651 this is the corrected file. Please evaluate it. Thanks! |
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In this commit - |
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@prakhar7651 Yes I have run the results with a corrected python script I will update the notebook if the results are better that's why I didn't upload it can you check how is the new result performing? |
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@prakhar7651 can you evaluate this now as today I think is the last day for submission? |
3 participants
I built a custom hybrid retrieval pipeline using SOTA models rather than the baseline FAISS approach.
System Details:
System Description: Hybrid Search pipeline combining dense embeddings (VoyageAI voyage-4-large 2048-dim) via Alibaba's Zvec database, with sparse lexical retrieval (BM25), fused together and passed through a cross-encoder reranker (VoyageAI Rerank-2.5).
System Type: Hybrid / RAG Retriever
Open Source: Not open source (rerankers and the embedding models are closed source, vector db and sparse retrieval is open source)
Looking forward to seeing the results on the leaderboard!
https://app.devrev.ai/devrev/works/ISS-269621/
ISS-269621